One of the advantages of the proposed symbolic methods is the compactness of the value functions, which can be demonstrated, for instance, on the 1DOF problem. The symbolic value function found by using the SVI-SNGP method (Figure REF , right) has 100 free parameters, while the baseline numerically approximated value function has 961 free parameters. An alternative reinforcement learning approach uses neural networks in the actor-critic scheme. The critic is approximated by a shallow neural network [1]} with 3791 parameters and by a deep network DDPG [2]} with 122101 parameters. The symbolic value function achieves the same or better performance with orders of magnitude fewer parameters.
